Teaching

Methods in Medical Informatics

SoSe 2018

  • Lecture: Medical Data Science (2 + 2 SWS => 6 ECTS)
    • This lecture comprises different areas of Medical Data Science. Data Science or statistical machine learning methods have the potential to transform personal health care over the coming years. Advances in the technologies have generated large biological data sets. In order to gain insights that can then be used to improve preventive care or treatment of patients, these big data have to be stored in a way that enables fast querying of relevant characteristics of the data and consequently building statistical models that represent the dependencies between variables. These models can then be utilized to derive new biomedical principals, provide evidence for or against certain hypotheses, and to assist medical professionals in their decision process. Specific topics are:
    • Gaining new insights from medical data
    • Modeling uncertainty in medical data science models
    • Making medical findings available through interpretable decision support systems
    • Method-wise, the lecture will introduce methods for GWAS analyses (e.g., LMMs), methods for sequence analysis (e.g., kernel methods), methods for “small n problems” (e.g., domain adaptation, transfer learning, and multitask learning), methods for data integration (advanced unsupervised learning methods), methods for learning probabilistic Machine Learning models (e.g., graphical models), methods for large data sets (e.g., deep learning models)
    • Go to Ilias course page: https://ovidius.uni-tuebingen.de/ilias3/goto.php?target=crs_1686451&client_id=pr02